summary(glm(s_whonum~S_women, data = field_analyze_full, family = "binomial"))##
## Call:
## glm(formula = s_whonum ~ S_women, family = "binomial", data = field_analyze_full)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.36301 0.77346 -1.762 0.0780 .
## S_women 0.04010 0.01605 2.498 0.0125 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 50.982 on 37 degrees of freedom
## Residual deviance: 42.788 on 36 degrees of freedom
## (172 observations deleted due to missingness)
## AIC: 46.788
##
## Number of Fisher Scoring iterations: 4summary(glm(s_whonum~S_women, data = field_analyze_mixedgend, family = "binomial"))##
## Call:
## glm(formula = s_whonum ~ S_women, family = "binomial", data = field_analyze_mixedgend)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.338580 1.096291 0.309 0.757
## S_women 0.006544 0.026998 0.242 0.808
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 14.421 on 10 degrees of freedom
## Residual deviance: 14.361 on 9 degrees of freedom
## (4 observations deleted due to missingness)
## AIC: 18.361
##
## Number of Fisher Scoring iterations: 4summary(lm(qualifications~S_women, data = field_analyze_full))##
## Call:
## lm(formula = qualifications ~ S_women, data = field_analyze_full)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0477 -0.1236 0.0281 0.9289 1.0595
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.931131 0.300513 19.737 <2e-16 ***
## S_women 0.002332 0.005274 0.442 0.66
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.182 on 62 degrees of freedom
## (146 observations deleted due to missingness)
## Multiple R-squared: 0.003145, Adjusted R-squared: -0.01293
## F-statistic: 0.1956 on 1 and 62 DF, p-value: 0.6598summary(lm(skills~S_women, data = field_analyze_full))##
## Call:
## lm(formula = skills ~ S_women, data = field_analyze_full)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2423 -0.2932 -0.2307 0.7347 0.7731
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.3037947 0.2426547 25.978 <2e-16 ***
## S_women -0.0007692 0.0042582 -0.181 0.857
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9542 on 62 degrees of freedom
## (146 observations deleted due to missingness)
## Multiple R-squared: 0.000526, Adjusted R-squared: -0.01559
## F-statistic: 0.03263 on 1 and 62 DF, p-value: 0.8572summary(lm(workload~S_women, data = field_analyze_full))##
## Call:
## lm(formula = workload ~ S_women, data = field_analyze_full)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3362 -0.3594 -0.2444 0.7162 1.7595
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.235914 0.319103 16.408 <2e-16 ***
## S_women 0.001543 0.005600 0.276 0.784
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.255 on 62 degrees of freedom
## (146 observations deleted due to missingness)
## Multiple R-squared: 0.001224, Adjusted R-squared: -0.01489
## F-statistic: 0.07596 on 1 and 62 DF, p-value: 0.7838summary(lm(fitneeds~S_women, data = field_analyze_full))##
## Call:
## lm(formula = fitneeds ~ S_women, data = field_analyze_full)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2811 -0.2811 -0.1399 0.7702 0.9140
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.075769 0.219105 27.730 <2e-16 ***
## S_women 0.002566 0.003845 0.667 0.507
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8616 on 62 degrees of freedom
## (146 observations deleted due to missingness)
## Multiple R-squared: 0.007134, Adjusted R-squared: -0.00888
## F-statistic: 0.4455 on 1 and 62 DF, p-value: 0.507summary(lm(candgend~S_women, data = field_analyze_full))##
## Call:
## lm(formula = candgend ~ S_women, data = field_analyze_full)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4389 -1.0493 0.2969 1.1443 2.0224
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.772568 0.362025 13.183 <2e-16 ***
## S_women 0.010251 0.006353 1.613 0.112
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.424 on 62 degrees of freedom
## (146 observations deleted due to missingness)
## Multiple R-squared: 0.0403, Adjusted R-squared: 0.02482
## F-statistic: 2.603 on 1 and 62 DF, p-value: 0.1117summary(lm(compdg~S_women, data = field_analyze_full))##
## Call:
## lm(formula = compdg ~ S_women, data = field_analyze_full)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4368 -1.4331 0.1152 1.1766 3.3996
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.32160 0.49692 6.684 7.54e-09 ***
## S_women 0.01115 0.00872 1.279 0.206
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.954 on 62 degrees of freedom
## (146 observations deleted due to missingness)
## Multiple R-squared: 0.0257, Adjusted R-squared: 0.009985
## F-statistic: 1.635 on 1 and 62 DF, p-value: 0.2057summary(lm(supcand~S_women, data = field_analyze_full))##
## Call:
## lm(formula = supcand ~ S_women, data = field_analyze_full)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5397 -0.5918 0.2909 0.5646 1.6688
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.852414 0.281679 20.777 <2e-16 ***
## S_women -0.005212 0.004943 -1.054 0.296
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.108 on 62 degrees of freedom
## (146 observations deleted due to missingness)
## Multiple R-squared: 0.01762, Adjusted R-squared: 0.001774
## F-statistic: 1.112 on 1 and 62 DF, p-value: 0.2958summary(lm(timewrksched~S_women, data = field_analyze_full))##
## Call:
## lm(formula = timewrksched ~ S_women, data = field_analyze_full)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6221 -0.6664 0.3927 0.6438 1.6143
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.237998 0.289189 18.113 <2e-16 ***
## S_women 0.005909 0.005075 1.164 0.249
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.137 on 62 degrees of freedom
## (146 observations deleted due to missingness)
## Multiple R-squared: 0.0214, Adjusted R-squared: 0.005618
## F-statistic: 1.356 on 1 and 62 DF, p-value: 0.2487summary(lm(qualifications~S_women, data = field_analyze_mixedgend))##
## Call:
## lm(formula = qualifications ~ S_women, data = field_analyze_mixedgend)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0531 -0.4091 0.1924 0.8037 0.9469
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.462295 0.540589 11.954 7.95e-07 ***
## S_women -0.008184 0.013016 -0.629 0.545
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.013 on 9 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.04208, Adjusted R-squared: -0.06436
## F-statistic: 0.3953 on 1 and 9 DF, p-value: 0.5451summary(lm(skills~S_women, data = field_analyze_mixedgend))##
## Call:
## lm(formula = skills ~ S_women, data = field_analyze_mixedgend)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.92135 -0.39034 0.07865 0.69591 1.07865
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.46042 0.50968 12.676 4.82e-07 ***
## S_women -0.01078 0.01227 -0.879 0.402
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9548 on 9 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.07899, Adjusted R-squared: -0.02334
## F-statistic: 0.7719 on 1 and 9 DF, p-value: 0.4025summary(lm(workload~S_women, data = field_analyze_mixedgend))##
## Call:
## lm(formula = workload ~ S_women, data = field_analyze_mixedgend)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0021 -0.7513 0.5496 0.9979 1.7772
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.25287 0.84023 6.252 0.000149 ***
## S_women -0.01003 0.02023 -0.496 0.631902
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.574 on 9 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.02659, Adjusted R-squared: -0.08157
## F-statistic: 0.2458 on 1 and 9 DF, p-value: 0.6319summary(lm(fitneeds~S_women, data = field_analyze_mixedgend))##
## Call:
## lm(formula = fitneeds ~ S_women, data = field_analyze_mixedgend)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.08053 -0.46020 0.04748 0.44507 1.15501
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.82451 0.43003 13.544 2.73e-07 ***
## S_women 0.00512 0.01035 0.495 0.633
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8056 on 9 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.02646, Adjusted R-squared: -0.08172
## F-statistic: 0.2446 on 1 and 9 DF, p-value: 0.6328summary(lm(candgend~S_women, data = field_analyze_mixedgend))##
## Call:
## lm(formula = candgend ~ S_women, data = field_analyze_mixedgend)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2000 -0.8689 0.7343 0.7558 1.7343
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.13199 0.86797 4.761 0.00103 **
## S_women 0.02267 0.02090 1.085 0.30614
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.626 on 9 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.1157, Adjusted R-squared: 0.01741
## F-statistic: 1.177 on 1 and 9 DF, p-value: 0.3061summary(lm(compdg~S_women, data = field_analyze_mixedgend))##
## Call:
## lm(formula = compdg ~ S_women, data = field_analyze_mixedgend)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.10244 -1.33702 0.08121 0.93845 2.43845
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.64333 0.87421 4.168 0.00242 **
## S_women 0.01836 0.02105 0.872 0.40562
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.638 on 9 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.07799, Adjusted R-squared: -0.02446
## F-statistic: 0.7612 on 1 and 9 DF, p-value: 0.4056summary(lm(supcand~S_women, data = field_analyze_mixedgend))##
## Call:
## lm(formula = supcand ~ S_women, data = field_analyze_mixedgend)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.85573 -0.28662 0.08736 0.48573 1.54265
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.02646 0.55590 10.84 1.82e-06 ***
## S_women -0.01138 0.01338 -0.85 0.417
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.041 on 9 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.07438, Adjusted R-squared: -0.02847
## F-statistic: 0.7232 on 1 and 9 DF, p-value: 0.4172summary(lm(timewrksched~S_women, data = field_analyze_mixedgend))##
## Call:
## lm(formula = timewrksched ~ S_women, data = field_analyze_mixedgend)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3289 -0.3920 0.6080 0.6396 0.6927
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.301880 0.519533 10.205 3.02e-06 ***
## S_women 0.001802 0.012509 0.144 0.889
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9733 on 9 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.0023, Adjusted R-squared: -0.1086
## F-statistic: 0.02075 on 1 and 9 DF, p-value: 0.8886summary(glm(s_whonum~S_val, data = field_analyze_full, family = "binomial"))##
## Call:
## glm(formula = s_whonum ~ S_val, family = "binomial", data = field_analyze_full)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.3000 0.9348 -0.321 0.748
## S_val 0.1415 0.1729 0.818 0.413
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 57.713 on 42 degrees of freedom
## Residual deviance: 57.038 on 41 degrees of freedom
## (167 observations deleted due to missingness)
## AIC: 61.038
##
## Number of Fisher Scoring iterations: 4summary(lm(qualifications~S_val, data = field_analyze_full))##
## Call:
## lm(formula = qualifications ~ S_val, data = field_analyze_full)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8566 -0.2700 0.1434 0.8678 1.5567
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.30546 0.48647 10.906 <2e-16 ***
## S_val 0.13779 0.08602 1.602 0.114
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.141 on 71 degrees of freedom
## (137 observations deleted due to missingness)
## Multiple R-squared: 0.03488, Adjusted R-squared: 0.02129
## F-statistic: 2.566 on 1 and 71 DF, p-value: 0.1136summary(lm(skills~S_val, data = field_analyze_full))##
## Call:
## lm(formula = skills ~ S_val, data = field_analyze_full)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1950 -0.4000 0.4974 0.6000 1.1126
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.78491 0.38729 14.937 <2e-16 ***
## S_val 0.10252 0.06848 1.497 0.139
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9084 on 71 degrees of freedom
## (137 observations deleted due to missingness)
## Multiple R-squared: 0.0306, Adjusted R-squared: 0.01695
## F-statistic: 2.241 on 1 and 71 DF, p-value: 0.1388summary(lm(workload~S_val, data = field_analyze_full))##
## Call:
## lm(formula = workload ~ S_val, data = field_analyze_full)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2795 -0.4965 0.5035 0.6481 1.9374
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.99027 0.53629 9.305 6.42e-14 ***
## S_val 0.07232 0.09482 0.763 0.448
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.258 on 71 degrees of freedom
## (137 observations deleted due to missingness)
## Multiple R-squared: 0.008126, Adjusted R-squared: -0.005844
## F-statistic: 0.5817 on 1 and 71 DF, p-value: 0.4482summary(lm(fitneeds~S_val, data = field_analyze_full))##
## Call:
## lm(formula = fitneeds ~ S_val, data = field_analyze_full)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1558 -0.3452 -0.1558 0.7180 1.0336
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.90324 0.35368 16.69 <2e-16 ***
## S_val 0.06313 0.06254 1.01 0.316
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8296 on 71 degrees of freedom
## (137 observations deleted due to missingness)
## Multiple R-squared: 0.01415, Adjusted R-squared: 0.0002664
## F-statistic: 1.019 on 1 and 71 DF, p-value: 0.3161summary(lm(candgend~S_val, data = field_analyze_full))##
## Call:
## lm(formula = candgend ~ S_val, data = field_analyze_full)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0269 -1.1987 0.4577 0.9731 2.4886
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.3396 0.6141 7.066 8.88e-10 ***
## S_val 0.1718 0.1086 1.582 0.118
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.44 on 71 degrees of freedom
## (137 observations deleted due to missingness)
## Multiple R-squared: 0.03406, Adjusted R-squared: 0.02045
## F-statistic: 2.503 on 1 and 71 DF, p-value: 0.1181summary(lm(compdg~S_val, data = field_analyze_full))##
## Call:
## lm(formula = compdg ~ S_val, data = field_analyze_full)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8266 -1.7123 0.2305 1.2305 3.5163
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.42651 0.81715 4.193 7.92e-05 ***
## S_val 0.05716 0.14502 0.394 0.695
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.91 on 70 degrees of freedom
## (138 observations deleted due to missingness)
## Multiple R-squared: 0.002214, Adjusted R-squared: -0.01204
## F-statistic: 0.1553 on 1 and 70 DF, p-value: 0.6947summary(lm(supcand~S_val, data = field_analyze_full))##
## Call:
## lm(formula = supcand ~ S_val, data = field_analyze_full)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6740 -0.6547 0.3453 0.3839 1.4418
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.53884 0.45446 12.19 <2e-16 ***
## S_val 0.01931 0.08036 0.24 0.811
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.066 on 71 degrees of freedom
## (137 observations deleted due to missingness)
## Multiple R-squared: 0.0008123, Adjusted R-squared: -0.01326
## F-statistic: 0.05772 on 1 and 71 DF, p-value: 0.8108summary(lm(timewrksched~S_val, data = field_analyze_full))##
## Call:
## lm(formula = timewrksched ~ S_val, data = field_analyze_full)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5599 -0.5599 0.3699 0.5803 1.7908
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.13911 0.50434 10.190 1.55e-15 ***
## S_val 0.07014 0.08917 0.787 0.434
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.183 on 71 degrees of freedom
## (137 observations deleted due to missingness)
## Multiple R-squared: 0.008638, Adjusted R-squared: -0.005325
## F-statistic: 0.6186 on 1 and 71 DF, p-value: 0.4342summary(glm(s_whonum~S_val, data = field_analyze_mixedgend, family = "binomial"))##
## Call:
## glm(formula = s_whonum ~ S_val, family = "binomial", data = field_analyze_mixedgend)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.0976 2.5042 -1.237 0.216
## S_val 0.6741 0.4608 1.463 0.143
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 16.301 on 11 degrees of freedom
## Residual deviance: 13.097 on 10 degrees of freedom
## (3 observations deleted due to missingness)
## AIC: 17.097
##
## Number of Fisher Scoring iterations: 4summary(lm(qualifications~S_val, data = field_analyze_mixedgend))##
## Call:
## lm(formula = qualifications ~ S_val, data = field_analyze_mixedgend)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4257 -0.4471 0.2332 0.6634 0.8534
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.9450 0.7226 6.844 4.49e-05 ***
## S_val 0.2403 0.1336 1.799 0.102
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8546 on 10 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.2445, Adjusted R-squared: 0.1689
## F-statistic: 3.236 on 1 and 10 DF, p-value: 0.1022summary(lm(skills~S_val, data = field_analyze_mixedgend))##
## Call:
## lm(formula = skills ~ S_val, data = field_analyze_mixedgend)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4257 -0.4471 0.2332 0.6634 0.8534
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.9450 0.7226 6.844 4.49e-05 ***
## S_val 0.2403 0.1336 1.799 0.102
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8546 on 10 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.2445, Adjusted R-squared: 0.1689
## F-statistic: 3.236 on 1 and 10 DF, p-value: 0.1022summary(lm(workload~S_val, data = field_analyze_mixedgend))##
## Call:
## lm(formula = workload ~ S_val, data = field_analyze_mixedgend)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.78004 -0.91090 0.08045 0.94959 1.94094
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.5825 1.2233 5.381 0.00031 ***
## S_val -0.3605 0.2262 -1.594 0.14207
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.447 on 10 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.2026, Adjusted R-squared: 0.1228
## F-statistic: 2.54 on 1 and 10 DF, p-value: 0.1421summary(lm(fitneeds~S_val, data = field_analyze_mixedgend))##
## Call:
## lm(formula = fitneeds ~ S_val, data = field_analyze_mixedgend)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.12627 -0.35285 -0.07943 0.83859 1.06110
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.84521 0.69859 8.367 7.93e-06 ***
## S_val 0.04684 0.12917 0.363 0.724
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8262 on 10 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.01298, Adjusted R-squared: -0.08572
## F-statistic: 0.1315 on 1 and 10 DF, p-value: 0.7244summary(lm(candgend~S_val, data = field_analyze_mixedgend))##
## Call:
## lm(formula = candgend ~ S_val, data = field_analyze_mixedgend)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6029 -0.9715 -0.1558 0.6864 3.0550
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.2607 1.2881 2.531 0.0298 *
## S_val 0.3422 0.2382 1.437 0.1814
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.523 on 10 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.1711, Adjusted R-squared: 0.08819
## F-statistic: 2.064 on 1 and 10 DF, p-value: 0.1814summary(lm(compdg~S_val, data = field_analyze_mixedgend))##
## Call:
## lm(formula = compdg ~ S_val, data = field_analyze_mixedgend)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.29124 -1.28666 -0.03666 1.22607 2.75967
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.34216 1.48410 2.926 0.0151 *
## S_val -0.05092 0.27441 -0.186 0.8565
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.755 on 10 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.003431, Adjusted R-squared: -0.09623
## F-statistic: 0.03443 on 1 and 10 DF, p-value: 0.8565summary(lm(supcand~S_val, data = field_analyze_mixedgend))##
## Call:
## lm(formula = supcand ~ S_val, data = field_analyze_mixedgend)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.7291 -0.5906 0.3198 0.3931 1.2709
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.50102 0.87145 6.313 8.77e-05 ***
## S_val 0.03259 0.16113 0.202 0.844
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.031 on 10 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.004073, Adjusted R-squared: -0.09552
## F-statistic: 0.0409 on 1 and 10 DF, p-value: 0.8438summary(lm(timewrksched~S_val, data = field_analyze_mixedgend))##
## Call:
## lm(formula = timewrksched ~ S_val, data = field_analyze_mixedgend)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8493 -0.1726 -0.1018 0.4562 1.1507
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.6171 0.6580 10.056 1.51e-06 ***
## S_val -0.2525 0.1217 -2.076 0.0647 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7783 on 10 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.3011, Adjusted R-squared: 0.2312
## F-statistic: 4.308 on 1 and 10 DF, p-value: 0.06466